Typically, RL solutions in the literature are designed, trained, and evaluated in the same environment that has a specific distribution of (or even fixed) parameters. However, particular design choices tailored for a specific setting may not work as well or even be applicable when the parameters of the environment change significantly [1]}. We have observed the same problem in our earlier work [2]}, where we had to re-design the components of our solution (thus, also re-train) in order to target different vehicular environments. This turns into a serious limitation, considering the diverse environments that V2V communications need to support. Considering that the RL agent is trained “off-line”, i.e., before its deployment, it is desirable to learn a policy that is applicable to different environments of interest without further training. This would eliminate the need of training a new agent from scratch every time an unseen (yet similar) condition arises in the deployed environment. Furthermore, it would offer the possibility to train the agent in a simpler, simulated environment, saving from the burdens of real-world training.
